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Beyond Putting Data to Work Strapline


incremental revenue


This DIY retail client wanted to prove how a localised, demand led approach supported by data analytics could drive increased footfall, sales growth and increase customer loyalty/repeat visits. Beyond Analysis were engaged to provide customer and basket analysis and retail consulting to define the strategy and support trials across two of their seasonal categories.

What We Did

In order to bring a customer lens to the solution and understand customer preferences and habits at a local level we developed two customer segmentations. The first was a project based segmentation to identify the type of DIY project the customer was buying for. This was supported by a secondary segmentation that looked at the type of products being purchased that indicate the level of DIY experience or capability of the customer. We enriched these two segmentations by integrating third party data to understand what kind of homes the customers live in - housing stock, demographics, ownership status. These analytics outputs were then used to understand how customer needs, preferences and DIY capabilities differed by local market and what this meant for local range mixes and customer support in store.


With local store based category allocations and range plans based on local demographics and needs we developed three generic store type models to test how a more targeted range and service would impact performance. These trials were run over seven stores for 6 months and generated increased revenue of £3.3m.

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